This section introduces aspects that may help facilitate a better understanding of the disclosure. Accordingly, these statements are to be read in this light and are not to be understood as admissions about what is or is not prior art.
A significant fraction of the population suffers from refractive errors in their visual system. Correction is commonly in the form of eyeglasses or contacts having the needed optical correction for an individual.
One alternative explored in prior art is developing custom display hardware for providing improved sharpness and focus for observers. On the one hand, adaptive optics has emerged as a field that attempts to measure, in real-time, optical aberrations using custom refractive and reflective hardware. On the other hand, lightfield displays have recently been proposed to improve static long-term optical aberrations, such as defocus and astigmatism in human vision systems. An example is a lightfield display that dynamically adapts its content to the observer's specific condition, causing it to appear focused despite of his/her refractive vision problem. Presented prototypes can show imagery for a region of a person's field-of-view (FOV). Others have proposed multi-layer display hardware solutions that also yield imagery appearing sharper for viewers. Both techniques typically require the viewer to be carefully tracked or kept in a fixed position.
Referring to FIG. 1a, a block diagram denoting a typical optical system. An image is presented to the normal optical system which is processed by the system and generates a perceived image. Referring to FIG. 1b, an optical system with aberrations is depicted. Similarly, the image is presented to the system and a perceived image is thereby generated. However, the perceived image may include a variety of issues, e.g., lack of focus, blurriness. To exemplify this situation, suppose a subject with hyperopia is viewing the image in 1b; hyperopia, or farsightedness, refers to situations in which a subject cannot focus on nearby objects. The image presented is perceived as blurry. The aberration (in this case hyperopia) can be modeled as a convolution (FIG. 1c) of the original image as provided to the normal optical system. That is, the optical system with aberrations is equivalent to the normal optical system with the convolution block. Corrections can be based on a deconvolution of the image prior to it being presented to the optical system with aberrations (identified as pre-correction). Deconvolution is an operation to reverse the effect of convolution. Hence, the deconvolution process substantially cancels the aberration (i.e., convolution process) resulting in an improved perceived image (FIG. 1d).
An alternative method to hardware-based solutions is software-only approaches which in general use some form of deconvolution (note: some of the aforementioned custom display hardware may also use a form of deconvolution). Direct deconvolution methods (e.g., inverse filtering) are sensitive to noise in the data and impulse response, typically referred to as the system's point-spread-function (PSF). To improve upon this limitation, constraints are applied to an iterative deconvolution process (e.g., Weiner filtering or van Cittert's method). One common objective is to use constraints to regularize deconvolution by adding terms to the optimization in order to find a solution with desirable properties (i.e., low level of noise). A technique known as total variation (TV) based deconvolution is another form of regularized deconvolution that has been shown both experimentally and theoretically to be able to find (i.e., preserve) sharp edges in the solution image. The regularization term often includes the total variational norm of the optimized variable and at least one weight parameter.
The aforementioned deconvolution techniques are usually used for tasks where the solution image (i.e., the deconvolved image) has pixel values similar to those of the target image. However, the pixel values of a typical prior art approach pre-corrected image may not be in a fixed and valid pixel value range (e.g., [0.0, 1.0]). As a solution, techniques in prior art bring all pixel values computed by the deconvolution into a valid range by either clamping or re-scaling after the deconvolution computation. A consequence of this is very low contrast and potentially significant ringing in the imagery perceived by an observer. Ringing implies oscillations in image intensity appearing around sharp edges in the visual content. The rings emanate from such edges and appear essentially on top of other content producing a distracting visual artifact.
To illustrate previous approaches, FIG. 2 shows several representative solutions under the same amount of −2.5 D of defocus blur observed at 3 meters. Previous methods use either inverse Fourier transform or Weiner filtering with pixel values clamped or rescaled to the valid range (e.g., [0.0, 1.0]). Some methods perform additional denoising schemes or edge tapering as a post-process to reduce ringing—the results are slightly improved. The first and second columns of FIG. 2 show original and blurred images. The blurred images represent the images perceived by an optical system with aberrations. The third column contains precorrected images (i.e., images that have gone through some form of deconvolution and are ready to be presented to the optical system with the associated aberrations). The fourth and fifth columns are the synthetically convolved precorrected images as a representation of perceived images by the optical system with aberrations of the precorrected images and camera-captured pictures of the precorrected images under −2.5 diopters of blur; the blur is generated by physically added a premeasured lens in front of an otherwise focused camera. The purpose of showing the synthetically convolved images separate from the captured images is that synthetically convolved images may include physically unattainable pixel values (e.g., negative values, or values exceeding the maximum pixel value). Therefore, the captured images represent more realistic perceived images of the precorrected images.
The three rows of FIG. 2 are representative ideal results from the aforementioned methods using Wiener filtering. In the first row, the precorrected image computed by Weiner filtering has a dynamic range of [−19, +19] (note: the image that is presented is implicitly rescaled to [0.0,1.0]). This experiment was also repeated using a standard TV-based deconvolution and the result is similar. In the second and third rows, the dynamic range of the precorrected image is limited to [−9, +9] and to [−0.015, 0.985], respectively. In all rows, the convolved precorrected image is computed synthetically and thus pixel values outside [0.0, 1.0] can be used. The results are good though at lower contrast as the pixel range of the precorrected image is reduced. However, the captured precorrected images demonstrate how such pixel values cannot be used by a physical system. It should be noted that the third row is effectively the single layer solution implemented by prior art methods.
Therefore, there is an unmet need to provide a method and a system that can overcome the shortcomings of the deconvolution solutions provided in the prior art.